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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Multi-Component Joint Maintenance Decision for Electro-Hydraulic Servo Fatigue Testing Machine Based on Multi-Head

Peng Liu¹, Guotai Huang¹, Jialu Xi¹

¹School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China.

Sensors (Basel, Switzerland)

|July 15, 2026

Summary

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Electro-mechanical Systems

Electro-mechanical Systems

Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...

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A new multi-component joint maintenance method using deep reinforcement learning significantly cuts costs and failures in fatigue testing machines. This approach optimizes maintenance for critical components, enhancing machine reliability and data quality.

Area of Science:

Mechanical Engineering
Artificial Intelligence
Reliability Engineering

Background:

Critical components in electro-hydraulic servo material fatigue testing machines face challenges in maintenance decision-making due to poor state observability and prediction difficulties.
Heterogeneous degradation mechanisms and observation methods across components (bearing beam, fixture, sensors, hydraulic oil) complicate joint maintenance strategies.

Purpose of the Study:

To propose a novel multi-component joint maintenance decision-making method for fatigue testing machines.
To enhance the reliability and data quality of these critical testing systems through optimized maintenance.

Main Methods:

Developed a continuous-discrete hybrid state Markov decision process (HS-MDP) accounting for component heterogeneity.
Implemented a differentiated discrete action space with action masking for engineering feasibility constraints.

Keywords:

deep reinforcement learning electro-hydraulic servo fatigue testing machine hybrid-state Markov decision process multi-component joint maintenance opportunistic maintenance

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Last Updated: Jul 16, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Published on: October 28, 2022

Utilized a Branching Dueling Deep Q-Network (DQN) framework with an inter-component attention mechanism and multi-head outputs.

Incorporated a data-quality loss term into the reward function, linked to sensor degradation.

Main Results:

The proposed method reduced average annual total cost by 60.3% (vs. periodic maintenance) and 42.6% (vs. threshold-based CBM).
Annual failures decreased from 9.8 to 1.4 instances.
Data efficiency improved from 82.1% to 96.2%.

Conclusions:

The multi-component joint maintenance method effectively addresses challenges in fatigue testing machine maintenance.
Key design elements—action masking, inter-component attention, and data-quality loss—are crucial for performance.
The approach offers significant improvements in cost reduction, failure prevention, and data integrity.